CureMatch Inc., San Diego, California 92121, USA.
San Diego Supercomputer Center, University of California, San Diego, La Jolla, California 92093, USA; email:
Annu Rev Pharmacol Toxicol. 2020 Jan 6;60:353-369. doi: 10.1146/annurev-pharmtox-010919-023746. Epub 2019 Jul 26.
The most common applications of artificial intelligence (AI) in drug treatment have to do with matching patients to their optimal drug or combination of drugs, predicting drug-target or drug-drug interactions, and optimizing treatment protocols. This review outlines some of the recently developed AI methods aiding the drug treatment and administration process. Selection of the best drug(s) for a patient typically requires the integration of patient data, such as genetics or proteomics, with drug data, like compound chemical descriptors, to score the therapeutic efficacy of drugs. The prediction of drug interactions often relies on similarity metrics, assuming that drugs with similar structures or targets will have comparable behavior or may interfere with each other. Optimizing the dosage schedule for administration of drugs is performed using mathematical models to interpret pharmacokinetic and pharmacodynamic data. The recently developed and powerful models for each of these tasks are addressed, explained, and analyzed here.
人工智能（AI）在药物治疗中的最常见应用与将患者与最佳药物或药物组合相匹配、预测药物-靶点或药物-药物相互作用以及优化治疗方案有关。本文概述了一些最近开发的人工智能方法，可辅助药物治疗和管理过程。选择最适合患者的最佳药物通常需要整合患者数据（如遗传学或蛋白质组学）与药物数据（如化合物化学描述符），以评估药物的治疗效果。药物相互作用的预测通常依赖于相似性度量，假设具有相似结构或靶点的药物将具有类似的行为或可能相互干扰。优化药物给药的剂量方案是使用数学模型来解释药代动力学和药效学数据来完成的。这里介绍、解释和分析了最近为这些任务中的每一个任务开发的强大模型。
